We describe how interpretable boosting algorithms based on ridge-regularized generalized linear models can be used to analyze high-dimensional environmental data. We illustrate this by using environmental, social, human and biophysical data to predict the financial vulnerability of farmers in Chile and Tunisia against climate hazards. We show how group structures can be considered and how interactions can be found in high-dimensional datasets using a novel 2-step boosting approach. The advantages and efficacy of the proposed method are shown and discussed. Results indicate that the presence of interaction effects only improves predictive power when included in two-step boosting. The most important variable in predicting all types of vulnerabilities are natural assets. Other important variables are the type of irrigation, economic assets and the presence of crop damage of near farms.

Today text classification becomes critical task for concerned individuals for numerous purposes. Hence, several researches have been conducted to develop automatic text classification for national and international languages. However, the need for an automatic text categorization system for local languages is felt. The main aim of this study is to establish a Pashto automatic text classification system. In order to pursue this work, we built a Pashto corpus which is a collection of Pashto documents due to the unavailability of public datasets of Pashto text documents. Besides, this study compares several models containing both statistical and neural network machine learning techniques including Multilayer Perceptron (MLP), Support Vector Machine (SVM), K Nearest Neighbor (KNN), decision tree, gaussian na\"ive Bayes, multinomial na\"ive Bayes, random forest, and logistic regression to discover the most effective approach. Moreover, this investigation evaluates two different feature extraction methods including unigram, and Time Frequency Inverse Document Frequency (IFIDF). Subsequently, this research obtained average testing accuracy rate 94% using MLP classification algorithm and TFIDF feature extraction method in this context.

Multimodal emotion recognition identifies human emotions from various data modalities like video, text, and audio. However, we found that this task can be easily affected by noisy information that does not contain useful semantics. To this end, we present a novel paradigm that attempts to extract noise-resistant features in its pipeline and introduces a noise-aware learning scheme to effectively improve the robustness of multimodal emotion understanding. Our new pipeline, namely Noise-Resistant Multimodal Transformer (NORM-TR), mainly introduces a Noise-Resistant Generic Feature (NRGF) extractor and a Transformer for the multimodal emotion recognition task. In particular, we make the NRGF extractor learn a generic and disturbance-insensitive representation so that consistent and meaningful semantics can be obtained. Furthermore, we apply a Transformer to incorporate Multimodal Features (MFs) of multimodal inputs based on their relations to the NRGF. Therefore, the possible insensitive but useful information of NRGF could be complemented by MFs that contain more details. To train the NORM-TR properly, our proposed noise-aware learning scheme complements normal emotion recognition losses by enhancing the learning against noises. Our learning scheme explicitly adds noises to either all the modalities or a specific modality at random locations of a multimodal input sequence. We correspondingly introduce two adversarial losses to encourage the NRGF extractor to learn to extract the NRGFs invariant to the added noises, thus facilitating the NORM-TR to achieve more favorable multimodal emotion recognition performance. In practice, on several popular multimodal datasets, our NORM-TR achieves state-of-the-art performance and outperforms existing methods by a large margin, which demonstrates that the ability to resist noisy information is important for effective emotion recognition.

In this thesis, we develop methods to enhance the interpretability of recent representation learning techniques in natural language processing (NLP) while accounting for the unavailability of annotated data. We choose to leverage Variational Autoencoders (VAEs) due to their efficiency in relating observations to latent generative factors and their effectiveness in data-efficient learning and interpretable representation learning. As a first contribution, we identify and remove unnecessary components in the functioning scheme of semi-supervised VAEs making them faster, smaller and easier to design. Our second and main contribution is to use VAEs and Transformers to build two models with inductive bias to separate information in latent representations into understandable concepts without annotated data. The first model, Attention-Driven VAE (ADVAE), is able to separately represent and control information about syntactic roles in sentences. The second model, QKVAE, uses separate latent variables to form keys and values for its Transformer decoder and is able to separate syntactic and semantic information in its neural representations. In transfer experiments, QKVAE has competitive performance compared to supervised models and equivalent performance to a supervised model using 50K annotated samples. Additionally, QKVAE displays improved syntactic role disentanglement capabilities compared to ADVAE. Overall, we demonstrate that it is possible to enhance the interpretability of state-of-the-art deep learning architectures for language modeling with unannotated data in situations where text data is abundant but annotations are scarce.

Video appeared to show a drone being shot down over the Kremlin Wednesday, in what Russia says was an assassination attempt against President Vladimir Putin. The State Department says that it's "unable to confirm" the authenticity of a video purporting to show a drone attack on the Kremlin. State Department Principal Deputy Spokesperson Vedant Patel was responding to claims from Russian government officials who said that Ukrainian forces attempted to kill President Vladimir Putin by a failed drone attack. "We are aware of these, but unable to confirm the authenticity of this," Patel said during a press conference Wednesday. "We're continuing to assess this and confirm the authenticity."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.S.-led coalition carried out a drone strike Wednesday in northwestern Syria targeting a senior al-Qaida leader, the U.S. military said. The Syrian Observatory for Human Rights, an opposition war monitor, said the strike hit a chicken farm near the town of Harem, killing one person. It said the dead man has not been identified yet.

Fox News' Alex Hogan reports on Russia claiming Ukraine attacked the Kremlin in an attempt to assassinate Vladmir Putin as the war in Ukraine rages on. Russia has trimmed down annual Victory Day celebrations, with some claiming the Kremlin fears protests and dissent following continued and severe losses in Ukraine. Russian President Vladimir Putin has used the celebrations, which mark the Soviet Union's triumph over Nazi Germany in World War II, as propaganda opportunities. He used 2021 to warn that Russia's enemies once more followed "much of the ideology of the Nazis," a rallying cry he repeated throughout his invasion of Ukraine, and in 2022 he marched in the Immortal Regiment procession while holding a picture of his father in military attire. However, this year's celebrations will have much less fanfare as governors in Belgorod, Kursk, Voronezh, Oryol and Pskov as well as the Crimean Peninsula have all canceled their parades, The Guardian reported.

The 11th International Conference on Learning Representations (ICLR) is taking place this week in Kigali, Rwanda, the first time a major AI conference has taken place in-person in Africa. The program includes workshops, contributed talks, affinity group events, and socials. In addition, a total of six invited talks covered a broad range of topics. In this post we give a flavour of the first two of these presentations. Sofia Crepso is an artist who explores the interaction between biological systems and AI.

'COURAGE TO CALL' - Authorities credit arrest of Texas mass shooting suspect to vital tip-line clue. Experts weigh in on how the technology will affect work. Continue reading … CHEAP SHOTS - Prince Harry's comments that King Charles and Prince William cannot forgive. Continue reading … 'GET HIM OUT OF HERE' - Donald Trump kicks NBC reporter off plane, slams network as "fake news." BATTLEFIELD RULES - AI requires "new generation" of arms control deal to govern future warfighting, says Marine veteran lawmaker.

Fueled by the expressive power of deep neural networks, normalizing flows have achieved spectacular success in generative modeling, or learning to draw new samples from a distribution given a finite dataset of training samples. Normalizing flows have also been applied successfully to variational inference, wherein one attempts to learn a sampler based on an expression for the log-likelihood or energy function of the distribution, rather than on data. In variational inference, the unimodality of the reference Gaussian distribution used within the normalizing flow can cause difficulties in learning multimodal distributions. We introduce an extension of normalizing flows in which the Gaussian reference is replaced with a reference distribution that is constructed via a tensor network, specifically a matrix product state or tensor train. We show that by combining flows with tensor networks on difficult variational inference tasks, we can improve on the results obtained by using either tool without the other.